Q&A: Why averages fail to answer crucial questions about women and girls

Papa Seck, UN Women's chief statistician, sits down with Devex to discuss the promises and perils of new data sources and the power of disaggregated data. Photo by: UN Women

BARCELONA — When it comes to data, organizations must look beyond averages to uncover and address intersectional inequalities, said Papa Seck, UN Women’s chief statistician. This will help to ensure no one is left behind in efforts to achieve the Sustainable Development Goals, he added.

“You really need to go beyond averages and start looking at specific groups of women and girls most likely to be deprived and left behind.”

— Papa Seck, chief statistician, UN Women

“What we've found is that, essentially, a country may do well on average, but once you start drilling down and looking at who was most likely to be left behind, you can have the same groups essentially experiencing the same levels of quite a lot of deprivation in multiple areas,” Seck said, adding that gender tends to be at the center of intersectional inequalities.

“If you're talking about intersectional inequalities without actually looking at it from a gender perspective, we honestly think that, from the evidence we've seen, we're really missing the point.”

For example, a 2018 report by UN Women found that while the average primary school attendance in Nigeria was 66% among girls, that figure dropped to 12% for Hausa girls from rural areas and poor households. “[The findings] truly brought home the fact that you really need to go beyond averages and start looking at specific groups of women and girls most likely to be deprived and left behind,” Seck said.

Speaking to Devex, Seck discussed the difference that disaggregated data can make in identifying the needs of the most marginalized, how this data should be used, and the promise and perils that new data sources can bring.

This conversation has been edited for length and clarity.

How can disaggregated data be used to acquire a better understanding of the needs of the most marginalized people?

One problem with disaggregation is that if you take it just from a technical point of view, essentially you can disaggregate to no end. But if disaggregated data is really something to be looked at from a policy point of view, first look at, essentially, why do we need this information, and have a dialogue with civil society organizations or policymakers who really understand what information is needed in order to make a dent in policy.

Subnational information, group-level information, and so on really comes from the dialogue between producers and users in order to see exactly what data is needed for what policies and how to produce that data. Sometimes, it's not just disaggregation — it may be having to to collect brand new data. For instance, if you're looking at homelessness and housing policies, a household survey will not give you the information that you need.

Later this month, UN Women and the U.N. Statistics Division are organizing a global conference precisely on these issues, looking at it from a normative point of view. So what are the principles we should use for data disaggregation and, looking at intersectional inequalities, who should produce that data, what partnerships are needed, and how that data should be used?

The way that data should be used, obviously, first, I think there has to be some key human rights principles that need to be addressed. What are the human rights principles needed so that data does no harm? But also, who needs that data? What partnerships are needed between national statistical offices, nongovernmental organizations, and policymakers so that whatever data is produced can be taken and used for advocacy to inform policies? I think this should be ingrained in the policy apparatus of countries.

How important is it to create a common understanding among data users and producers on how to measure intersecting inequalities, specifically from a gender perspective?

I think it's absolutely critical, and the reason is that, essentially, if we don't have a common understanding, we can end up giving competing messages, and that can be dangerous because it can give the wrong message to policymakers. That can lead to a waste of resources.

But one thing that we have found is that even though we've been talking quite a lot about collaborating between different stakeholders and so, I think there's still a way to go, particularly forging collaboration between national statistical offices and CSOs. If you look at the private sector, for instance, all of these entities are now both producers and consumers of data.

Policymakers, through their programs, are also both consumers and producers. [We need to make sure] that we observe the same quality standards when we are producing data, making sure that the data we produce is not competing, but can be used to complement what each group is doing. I think that’s really fundamental: making sure that we have technology that can accommodate different data sources and different standards so that we can have information that can really be used to harness to inform policies.

What opportunities do new technologies and new data sources present in terms of measuring the SDGs?

I think [there are] immense [opportunities] beyond just measuring the SDGs but more toward essentially informing policies. Now that we are five years into the SDG process, we really need to go beyond just measurement and monitoring and into creating solutions. I think that's where new technologies and new data sources can really bring solutions into what we do: informing policies, basically doing things differently, and having new sources of information that can tell us what works and what doesn't.

However, there are also dangers and perils that can come with new technology. One thing that we've seen is, with new technologies, there can be the rise of harm. Violence against women in social media is an example that we've seen, with sexual harassment and sexual violence really rising to the fore. We see that people's privacy is being violated right, left, and center.

The rise of surveillance is another example. Sometimes we don't also understand what biases are inherent in new data sources and new technologies. Those are things we really need to get to the bottom of so that we can understand what the promises are, but also what things we should watch out for.

Is there anything you think the global development community should be considering when it comes to data and measuring what matters?

Essentially, my call to action is that we have quite a lot of players in this space and these players come from the private sector, from policy, civil society, et cetera. All the actors need to be in this in order to improve the experience of people. I think profit shouldn't be the driving force, and — I'm just going to put it bluntly — the human rights of people should be at the forefront, and governments need to take this seriously.

Devex, with support from our partner UN Women, is exploring how data is being used to inform policy and advocacy to advance gender equality. Gender data is crucial to make every woman and girl count. Visit the Focus on: Gender Data page for more. Disclaimer: the views in this article do not necessarily represent the views of UN Women.

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  • Rebecca Root

    Rebecca Root is a Reporter and Editorial Associate at Devex producing news stories, video, and podcasts as well as partnership content. She has a background in finance, travel, and global development journalism and has written for a variety of publications while living and working in New York, London, and Barcelona.